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Test shapes directly #1064

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merged 1 commit into from
Jun 7, 2023
Merged

Test shapes directly #1064

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22 changes: 11 additions & 11 deletions keras_nlp/layers/position_embedding_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -42,8 +42,8 @@ def test_static_layer_output_shape(self):

# When using static position embedding shapes, the output is expected
# to be the same as the input shape in all dimensions save batch.
expected_output_shape = [None, sequence_length, feature_size]
self.assertEqual(expected_output_shape, output_tensor.shape.as_list())
expected_output_shape = (None, sequence_length, feature_size)
self.assertEqual(expected_output_shape, output_tensor.shape)
# The output dtype for this layer should match the compute dtype.
self.assertEqual(test_layer.compute_dtype, output_tensor.dtype)

Expand All @@ -61,13 +61,13 @@ def test_more_than_3_dimensions_static(self):

# When using static position embedding shapes, the output is expected
# to be the same as the input shape in all dimensions save batch.
expected_output_shape = [
expected_output_shape = (
None,
feature_size,
sequence_length,
feature_size,
]
self.assertEqual(expected_output_shape, output_tensor.shape.as_list())
)
self.assertEqual(expected_output_shape, output_tensor.shape)
# The output dtype for this layer should match the compute dtype.
self.assertEqual(test_layer.compute_dtype, output_tensor.dtype)

Expand All @@ -83,8 +83,8 @@ def test_float16_dtype(self):

# When using static position embedding shapes, the output is expected
# to be the same as the input shape in all dimensions save batch.
expected_output_shape = [None, sequence_length, feature_size]
self.assertEqual(expected_output_shape, output_tensor.shape.as_list())
expected_output_shape = (None, sequence_length, feature_size)
self.assertEqual(expected_output_shape, output_tensor.shape)
# The default output dtype for this layer should be "float32".
self.assertEqual(tf.float16, output_tensor.dtype)

Expand All @@ -101,8 +101,8 @@ def test_dynamic_layer_output_shape(self):
# When using dynamic position embedding shapes, the output is expected
# to be the same as the input shape in all dimensions - but may be None
# if the input shape is None there.
expected_output_shape = [None, None, feature_size]
self.assertEqual(expected_output_shape, output_tensor.shape.as_list())
expected_output_shape = (None, None, feature_size)
self.assertEqual(expected_output_shape, output_tensor.shape)

def test_more_than_3_dimensions_dynamic(self):
max_sequence_length = 60
Expand All @@ -117,8 +117,8 @@ def test_more_than_3_dimensions_dynamic(self):
# When using dynamic position embedding shapes, the output is expected
# to be the same as the input shape in all dimensions - but may be None
# if the input shape is None there.
expected_output_shape = [None, None, None, feature_size]
self.assertEqual(expected_output_shape, output_tensor.shape.as_list())
expected_output_shape = (None, None, None, feature_size)
self.assertEqual(expected_output_shape, output_tensor.shape)

def test_dynamic_layer_slicing(self):
max_sequence_length = 40
Expand Down
12 changes: 6 additions & 6 deletions keras_nlp/layers/sine_position_encoding_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -40,8 +40,8 @@ def test_static_layer_output_shape(self):

# When using static positional encoding shapes, the output is expected
# to be the same as the input shape in all dimensions.
expected_output_shape = [None, seq_length, hidden_size]
self.assertEqual(expected_output_shape, outputs.shape.as_list())
expected_output_shape = (None, seq_length, hidden_size)
self.assertEqual(expected_output_shape, outputs.shape)

def test_dynamic_layer_output_shape(self):
pos_encoding = sine_position_encoding.SinePositionEncoding()
Expand All @@ -51,8 +51,8 @@ def test_dynamic_layer_output_shape(self):

# When using dynamic positional encoding shapes, the output is expected
# to be the same as the input shape in all dimensions but may be None.
expected_output_shape = [None, None, hidden_size]
self.assertEqual(expected_output_shape, outputs.shape.as_list())
expected_output_shape = (None, None, hidden_size)
self.assertEqual(expected_output_shape, outputs.shape)

# do multi dimension before sequence length
def test_multi_dimension_layer_output_shape(self):
Expand All @@ -64,8 +64,8 @@ def test_multi_dimension_layer_output_shape(self):

# When using muliple dimensions before sequence length, the output is
# expected to be the same as the input shape in all dimensions.
expected_output_shape = [None, None, seq_length, hidden_size]
self.assertEqual(expected_output_shape, outputs.shape.as_list())
expected_output_shape = (None, None, seq_length, hidden_size)
self.assertEqual(expected_output_shape, outputs.shape)

def test_output_correct_values(self):
pos_encoding = sine_position_encoding.SinePositionEncoding()
Expand Down